
import pandas as pd
import numpy as np
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline 

import warnings
warnings.filterwarnings("ignore")


pd.set_option('display.max_columns',None)
pd.set_option('display.max_rows',None)
#pd.set_option('precision',3)


generation_data = pd.read_csv("Plant_1_Generation_Data.csv")


generation_data.sample(5).style.set_properties(
    **{
        'background-color': 'OliveDrab',
        'color': 'white',
        'border-color': 'darkblack'
    })


weather_data = pd.read_csv("Plant_1_Weather_Sensor_Data.csv")


weather_data.sample(5).style.set_properties(
    **{
        'background-color': 'pink',
        'color': 'Black',
        'border-color': 'darkblack'
    })


def parse_datetime(date_str):
    for fmt in ('%Y-%m-%d %H:%M', '%Y-%m-%d %H:%M:%S', '%d-%m-%Y %H:%M', '%d-%m-%Y %H:%M:%S'):
        try:
            return pd.to_datetime(date_str, format=fmt)
        except ValueError:
            pass
    raise ValueError(f"No valid date format found for {date_str}")

generation_data['DATE_TIME'] = generation_data['DATE_TIME'].apply(parse_datetime)
weather_data['DATE_TIME'] = weather_data['DATE_TIME'].apply(parse_datetime)

generation_data['DATE_TIME'] = generation_data['DATE_TIME'].dt.strftime('%Y-%m-%d %H:%M')
weather_data['DATE_TIME'] = weather_data['DATE_TIME'].dt.strftime('%Y-%m-%d %H:%M:%S')


generation_data['DATE_TIME'] = pd.to_datetime(generation_data['DATE_TIME'],format = '%Y-%m-%d %H:%M')
weather_data['DATE_TIME'] = pd.to_datetime(weather_data['DATE_TIME'],format = '%Y-%m-%d %H:%M:%S')


df_solar = pd.merge(generation_data.drop(columns = ['PLANT_ID']), weather_data.drop(columns = ['PLANT_ID', 'SOURCE_KEY']), on='DATE_TIME')
df_solar.sample(5).style.background_gradient(cmap='cool')


# adding separate time and date columns
df_solar["DATE"] = pd.to_datetime(df_solar["DATE_TIME"]).dt.date
df_solar["TIME"] = pd.to_datetime(df_solar["DATE_TIME"]).dt.time
df_solar['DAY'] = pd.to_datetime(df_solar['DATE_TIME']).dt.day
df_solar['MONTH'] = pd.to_datetime(df_solar['DATE_TIME']).dt.month
df_solar['WEEK'] = pd.to_datetime(df_solar['DATE_TIME']).dt.week


# add hours and minutes for ml models
df_solar['HOURS'] = pd.to_datetime(df_solar['TIME'],format='%H:%M:%S').dt.hour
df_solar['MINUTES'] = pd.to_datetime(df_solar['TIME'],format='%H:%M:%S').dt.minute
df_solar['TOTAL MINUTES PASS'] = df_solar['MINUTES'] + df_solar['HOURS']*60


# add date as string column
df_solar["DATE_STRING"] = df_solar["DATE"].astype(str) # add column with date as string
df_solar["HOURS"] = df_solar["HOURS"].astype(str)
df_solar["TIME"] = df_solar["TIME"].astype(str)

df_solar.head(2)


df_solar.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 68774 entries, 0 to 68773
Data columns (total 18 columns):
 #   Column               Non-Null Count  Dtype         
---  ------               --------------  -----         
 0   DATE_TIME            68774 non-null  datetime64[ns]
 1   SOURCE_KEY           68774 non-null  object        
 2   DC_POWER             68774 non-null  float64       
 3   AC_POWER             68774 non-null  float64       
 4   DAILY_YIELD          68774 non-null  float64       
 5   TOTAL_YIELD          68774 non-null  float64       
 6   AMBIENT_TEMPERATURE  68774 non-null  float64       
 7   MODULE_TEMPERATURE   68774 non-null  float64       
 8   IRRADIATION          68774 non-null  float64       
 9   DATE                 68774 non-null  object        
 10  TIME                 68774 non-null  object        
 11  DAY                  68774 non-null  int64         
 12  MONTH                68774 non-null  int64         
 13  WEEK                 68774 non-null  int64         
 14  HOURS                68774 non-null  object        
 15  MINUTES              68774 non-null  int64         
 16  TOTAL MINUTES PASS   68774 non-null  int64         
 17  DATE_STRING          68774 non-null  object        
dtypes: datetime64[ns](1), float64(7), int64(5), object(5)
memory usage: 10.0+ MB


df_solar.isnull().sum()

DATE_TIME              0
SOURCE_KEY             0
DC_POWER               0
AC_POWER               0
DAILY_YIELD            0
TOTAL_YIELD            0
AMBIENT_TEMPERATURE    0
MODULE_TEMPERATURE     0
IRRADIATION            0
DATE                   0
TIME                   0
DAY                    0
MONTH                  0
WEEK                   0
HOURS                  0
MINUTES                0
TOTAL MINUTES PASS     0
DATE_STRING            0
dtype: int64


df_solar.shape

(68774, 18)


# there are no nulls or blank values in the dataset


df_solar.describe().style.background_gradient(cmap='rainbow')


#from sklearn.preprocessing import LabelEncoder
#encoder = LabelEncoder()
#df_solar['SOURCE_KEY_NUMBER'] = encoder.fit_transform(df_solar['SOURCE_KEY'])
#df_solar.head()
#df_solar


from sklearn.preprocessing import LabelEncoder

# Initialize the LabelEncoder
encoder = LabelEncoder()

# Encode the entire column first
encoded_values = encoder.fit_transform(df_solar['SOURCE_KEY'])

# Initialize the new column with default values
df_solar['SOURCE_KEY_NUMBER'] = -1

# Process data in chunks
chunk_size = 10000  # Define a chunk size
num_chunks = (len(df_solar) // chunk_size) + 1

for i in range(num_chunks):
    start = i * chunk_size
    end = start + chunk_size
    if end > len(df_solar):  # Ensure the last chunk does not exceed the length
        end = len(df_solar)
    df_solar.iloc[start:end, df_solar.columns.get_loc('SOURCE_KEY_NUMBER')] = encoded_values[start:end]

print(df_solar.head())

   DATE_TIME       SOURCE_KEY  DC_POWER  AC_POWER  DAILY_YIELD  TOTAL_YIELD  \
0 2020-05-15  1BY6WEcLGh8j5v7       0.0       0.0          0.0    6259559.0   
1 2020-05-15  1IF53ai7Xc0U56Y       0.0       0.0          0.0    6183645.0   
2 2020-05-15  3PZuoBAID5Wc2HD       0.0       0.0          0.0    6987759.0   
3 2020-05-15  7JYdWkrLSPkdwr4       0.0       0.0          0.0    7602960.0   
4 2020-05-15  McdE0feGgRqW7Ca       0.0       0.0          0.0    7158964.0   

   AMBIENT_TEMPERATURE  MODULE_TEMPERATURE  IRRADIATION        DATE      TIME  \
0            25.184316           22.857507          0.0  2020-05-15  00:00:00   
1            25.184316           22.857507          0.0  2020-05-15  00:00:00   
2            25.184316           22.857507          0.0  2020-05-15  00:00:00   
3            25.184316           22.857507          0.0  2020-05-15  00:00:00   
4            25.184316           22.857507          0.0  2020-05-15  00:00:00   

   DAY  MONTH  WEEK HOURS  MINUTES  TOTAL MINUTES PASS DATE_STRING  \
0   15      5    20     0        0                   0  2020-05-15   
1   15      5    20     0        0                   0  2020-05-15   
2   15      5    20     0        0                   0  2020-05-15   
3   15      5    20     0        0                   0  2020-05-15   
4   15      5    20     0        0                   0  2020-05-15   

   SOURCE_KEY_NUMBER  
0                  0  
1                  1  
2                  2  
3                  3  
4                  4


file_name = 'df_solar.csv'
df_solar.to_csv(file_name, index=False)


df_solar['DATE'].nunique() #the data is daily for 34 days

34


solar_dc = df_solar.pivot_table(values='DC_POWER', index='TIME', columns='DATE')

def Daywise_plot_dc(data= None, row = None, col = None, title='DC Power'):
    cols = data.columns # take all column
    gp = plt.figure(figsize=(20,40)) 
    
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    for i in range(1, len(cols)+1):
        ax = gp.add_subplot(row,col, i)
        data[cols[i-1]].plot(ax=ax, color='red')
        ax.set_title('{} {}'.format(title, cols[i-1]),color='blue')
        
Daywise_plot_dc(data=solar_dc, row=12, col=3)


solar_ac = df_solar.pivot_table(values='AC_POWER', index='TIME', columns='DATE')

def Daywise_plot_ac(data= None, row = None, col = None, title='AC Power'):
    cols = data.columns # take all column
    gp = plt.figure(figsize=(20,40)) 
    
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    for i in range(1, len(cols)+1):
        ax = gp.add_subplot(row,col, i)
        data[cols[i-1]].plot(ax=ax, color='red')
        ax.set_title('{} {}'.format(title, cols[i-1]),color='blue')
        
Daywise_plot_ac(data=solar_ac, row=12, col=3)


def Daywise_plot_dc_ac(dc_data=None, ac_data=None, row=None, col=None, title='Power'):
    cols = dc_data.columns  # take all columns
    gp = plt.figure(figsize=(20, 40)) 
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    
    for i in range(1, len(cols) + 1):
        ax = gp.add_subplot(row, col, i)
        dc_data[cols[i - 1]].plot(ax=ax, color='red', label='DC Power')
        ac_data[cols[i - 1]].plot(ax=ax, color='green', label='AC Power')
        ax.set_title('{} {}'.format(title, cols[i - 1]), color='blue')
        ax.legend()

Daywise_plot_dc_ac(dc_data=solar_dc, ac_data=solar_ac, row=12, col=3)


daily_dc = df_solar.groupby('DATE')['DC_POWER'].agg('sum')

ax = daily_dc.sort_values(ascending=False).plot.bar(figsize=(17,5), legend=True,color='red')
plt.title('Daily DC Power')
plt.show()


daily_ac = df_solar.groupby('DATE')['AC_POWER'].agg('sum')

ax = daily_ac.sort_values(ascending=False).plot.bar(figsize=(17,5), legend=True,color='red')
plt.title('Daily AC Power')
plt.show()


# Combine both daily sums into a single DataFrame
daily_power = pd.DataFrame({'DC_POWER': daily_dc, 'AC_POWER': daily_ac})

# Plot the grouped bar plot
ax = daily_power.sort_values(by='DC_POWER', ascending=False).plot.bar(
    figsize=(17, 5), color=['red', 'green']
)
plt.title('Daily DC and AC Power')
plt.xlabel('Date')
plt.ylabel('Power')
plt.legend(['DC Power', 'AC Power'])
plt.show()


solar_irradiation = df_solar.pivot_table(values='IRRADIATION', index='TIME', columns='DATE')

def Daywise_plot(data= None, row = None, col = None, title='IRRADIATION'):
    cols = data.columns # take all column
    gp = plt.figure(figsize=(20,40)) 
    
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    for i in range(1, len(cols)+1):
        ax = gp.add_subplot(row,col, i)
        data[cols[i-1]].plot(ax=ax, color='blue')
        ax.set_title('{} {}'.format(title, cols[i-1]),color='blue')
        
Daywise_plot(data=solar_irradiation, row=12, col=3)


def Daywise_plot_dc_irradiation(dc_data=None, irrad_data=None, row=None, col=None, title='DC Vs Irradiation'):
    cols = dc_data.columns  # take all columns
    gp = plt.figure(figsize=(20, 40)) 
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    
    for i in range(1, len(cols) + 1):
        ax = gp.add_subplot(row, col, i)
        dc_data[cols[i - 1]].plot(ax=ax, color='red', label='DC Power')
        irrad_data[cols[i - 1]].plot(ax=ax, color='green', label='Irradiation')
        ax.set_title('{} {}'.format(title, cols[i - 1]), color='blue')
        ax.legend()

Daywise_plot_dc_irradiation(dc_data=solar_dc, irrad_data=solar_irradiation, row=12, col=3)


daily_irradiation = df_solar.groupby('DATE')['IRRADIATION'].agg('sum')

daily_irradiation.sort_values(ascending=False).plot.bar(figsize=(17,5), legend=True,color='blue')
plt.title('IRRADIATION')
plt.show()


# Combine both daily sums into a single DataFrame
daily_dcpower_irrad = pd.DataFrame({'DC_POWER': daily_dc, 'IRRADIATION': daily_irradiation})

# Plot the grouped bar plot
ax = daily_power.sort_values(by='DC_POWER', ascending=False).plot.bar(
    figsize=(17, 5), color=['red', 'green']
)
plt.title('Daily DC Power and Irradiation')
plt.xlabel('Date')
plt.ylabel('Power Vs Irradiation')
plt.legend(['DC Power', 'Irradiation'])
plt.show()


#sns.displot(data=df_solar, x="AMBIENT_TEMPERATURE", kde=True, bins = 100,color = "red", facecolor = "#3F7F7F",height = 5, aspect = 3.5);


solar_ambiant_temp = df_solar.pivot_table(values='AMBIENT_TEMPERATURE', index='TIME', columns='DATE')
solar_module_temp = df_solar.pivot_table(values='MODULE_TEMPERATURE', index='TIME', columns='DATE')

def Daywise_plot_am_mo(am_data=None, mo_data=None, row=None, col=None, title='Ambient Vs Module'):
    cols = am_data.columns  # take all columns
    gp = plt.figure(figsize=(20, 40)) 
    gp.subplots_adjust(wspace=0.2, hspace=0.5)
    
    for i in range(1, len(cols) + 1):
        ax = gp.add_subplot(row, col, i)
        am_data[cols[i - 1]].plot(ax=ax, color='red', label='Ambient Temperature')
        mo_data[cols[i - 1]].plot(ax=ax, color='green', label='Module Temperature')
        ax.set_title('{} {}'.format(title, cols[i - 1]), color='blue')
        ax.legend()

Daywise_plot_am_mo(am_data=solar_ambiant_temp, mo_data=solar_module_temp, row=12, col=3)


daily_am = df_solar.groupby('DATE')['AMBIENT_TEMPERATURE'].agg('sum')
daily_mo = df_solar.groupby('DATE')['MODULE_TEMPERATURE'].agg('sum')

#daily_ambient_temp.sort_values(ascending=False).plot.bar(figsize=(17,5), legend=True,color='darkgreen')
#plt.title('AMBIENT_TEMPERATURE')
#plt.show()

daily_ambient_module_temp = pd.DataFrame({'AMBIENT_TEMPERATURE': daily_am, 'MODULE_TEMPERATURE': daily_mo})

# Plot the grouped bar plot
ax = daily_ambient_module_temp.sort_values(by='MODULE_TEMPERATURE', ascending=False).plot.bar(
    figsize=(17, 5), color=['red', 'green']
)
plt.title('Daily Ambient and Module Temperature')
plt.xlabel('Date')
plt.ylabel('Ambient Vs Module')
plt.legend(['Ambient', 'Module'])
plt.show()


plt.figure(figsize=(16,16))

date=["2020-05-25"]

plt.subplot(411)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME,
             y=df_solar[df_solar["DATE_STRING"].isin(date)].DC_POWER,
             label="DC_Power_Best",
             color='green')

plt.subplot(412)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME,
             y=df_solar[df_solar["DATE_STRING"].isin(date)].AC_POWER,
             label="AC_Power_Best",
             color='green')

plt.subplot(413)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].IRRADIATION, 
             label="Irridation_Best",
             color='green');
plt.title("Irradiation : {}" .format(date[0]))

plt.subplot(414)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].AMBIENT_TEMPERATURE, 
             label="Ambient_Temperature_Best",
             color='green');
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].MODULE_TEMPERATURE, 
             label="Module_Temperature_Best",
             color='blue');
plt.title("Module Temperature & Ambient Temperature: {}" .format(date[0]));

plt.tight_layout()
plt.show()


date=["2020-05-18"]
plt.figure(figsize=(16,16))

plt.subplot(411)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].DC_POWER, 
             label="DC_Power_Worst",
             color='red');
plt.title("DC Power Generation: {}" .format(date[0]))

plt.subplot(412)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME,
             y=df_solar[df_solar["DATE_STRING"].isin(date)].AC_POWER,
             label="AC_Power_Worst",
             color='red')


plt.subplot(413)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].IRRADIATION, 
             label="Irridation_Worst",
             color='red');
plt.title("Irradiation : {}" .format(date[0]))

plt.subplot(414)
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].AMBIENT_TEMPERATURE, 
             label="Ambient_Temperature_Worst",
             color='red');
sns.lineplot(x=df_solar[df_solar["DATE_STRING"].isin(date)].DATE_TIME, 
             y=df_solar[df_solar["DATE_STRING"].isin(date)].MODULE_TEMPERATURE, 
             label="Module_Temperature_Worst",
             color='blue');
plt.title("Module Temperature & Ambient Temperature: {}" .format(date[0]));

plt.tight_layout()
plt.show()


df_solar['SOURCE_KEY'].unique()

array(['1BY6WEcLGh8j5v7', '1IF53ai7Xc0U56Y', '3PZuoBAID5Wc2HD',
       '7JYdWkrLSPkdwr4', 'McdE0feGgRqW7Ca', 'VHMLBKoKgIrUVDU',
       'WRmjgnKYAwPKWDb', 'ZnxXDlPa8U1GXgE', 'ZoEaEvLYb1n2sOq',
       'adLQvlD726eNBSB', 'bvBOhCH3iADSZry', 'iCRJl6heRkivqQ3',
       'ih0vzX44oOqAx2f', 'pkci93gMrogZuBj', 'rGa61gmuvPhdLxV',
       'sjndEbLyjtCKgGv', 'uHbuxQJl8lW7ozc', 'wCURE6d3bPkepu2',
       'z9Y9gH1T5YWrNuG', 'zBIq5rxdHJRwDNY', 'zVJPv84UY57bAof',
       'YxYtjZvoooNbGkE'], dtype=object)


filter_date = '2020-06-06 08:30:00'

filtered_df = df_solar[df_solar['DATE_TIME'] == filter_date]

# Create the plot
plt.figure(figsize=(14, 8))

# Plotting DC Power Output
plt.plot(filtered_df['SOURCE_KEY'], filtered_df['DC_POWER'], marker='o', linestyle='-', color='g', label='DC Power Input')

# Plotting AC Power Output
plt.plot(filtered_df['SOURCE_KEY'], filtered_df['AC_POWER'], marker='o', linestyle='-', color='r', label='AC Power Output')

# Adding title and labels
plt.title(f'Power vs Inverter ID on {filter_date}')
plt.xlabel('Inverter ID')
plt.ylabel('Power (kW)')

# Rotate x-axis labels for better readability
plt.xticks(rotation=90)

# Add legend
plt.legend()

# Display the plot
plt.grid(True)
plt.tight_layout()
plt.show()


solar_dc_power = df_solar[df_solar['DC_POWER'] > 0]['DC_POWER'].values
solar_ac_power = df_solar[df_solar['AC_POWER'] > 0]['AC_POWER'].values


solar_plant_eff = (np.max(solar_ac_power)/np.max(solar_dc_power ))*100
print(f"Power ratio AC/DC (Efficiency) of Solar Power Plant:  {solar_plant_eff:0.3f} %")

Power ratio AC/DC (Efficiency) of Solar Power Plant:  9.750 %


AC_list=[]
for i in df_solar['AC_POWER']:
    if i>0:
        AC_list.append(i)
AC_list
#AC_list.sort()
#AC_list.reverse()
len(AC_list)

36823


DC_list=[]
for i in df_solar['DC_POWER']:
    if i>0:
        DC_list.append(i)
DC_list
DC_list.sort()
DC_list.reverse()
len(DC_list)

36823


plt.figure(figsize=(16,8))
AC_list.sort()
DC_list.sort()
#print(DC_list)
#DC_list.sort
#res = [i / 10 for i in AC_list]
eff = [i/j for i,j in zip(AC_list,DC_list)]

plt.plot(AC_list,eff,color='green')
plt.xlabel('Output power in kW')
plt.ylabel('efficiency AC/DC')
plt.title('Output power vs efficiency');


df2 = df_solar.copy()
X = df2[['DAILY_YIELD','TOTAL_YIELD','AMBIENT_TEMPERATURE','MODULE_TEMPERATURE','IRRADIATION','DC_POWER']]
y = df2['AC_POWER']


X.head()


y.head()

0    0.0
1    0.0
2    0.0
3    0.0
4    0.0
Name: AC_POWER, dtype: float64


from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.2,random_state=21)


from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

lr_clf = LinearRegression()
lr_clf.fit(X_train,y_train)
score_lr = 100*lr_clf.score(X_test,y_test)
print(f'LR Model score = {score_lr:4.4f}%')

LR Model score = 99.9995%


lr = LinearRegression()
lr.fit(X_train,y_train)
y_pred_lr = lr.predict(X_test)
R2_Score_lr = round(r2_score(y_pred_lr,y_test) * 100, 2)

print("R2 Score : ",R2_Score_lr,"%")

R2 Score :  100.0 %


from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor()
rfr.fit(X_train,y_train)
y_pred_rfr = rfr.predict(X_test)
R2_Score_rfr = round(r2_score(y_pred_rfr,y_test) * 100, 2)

print("R2 Score : ",R2_Score_rfr,"%")

R2 Score :  100.0 %


from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
dtr.fit(X_train,y_train)

y_pred_dtr = rfr.predict(X_test)
R2_Score_dtr = round(r2_score(y_pred_dtr,y_test) * 100, 2)

print("R2 Score : ",R2_Score_dtr,"%")

R2 Score :  100.0 %


prediction = rfr.predict(X_test)
print(prediction)

[   0.         1071.24303578  299.62135714 ...  669.40498213  377.82001786
  117.4835    ]


cross_checking = pd.DataFrame({'Actual' : y_test , 'Predicted' : prediction})
cross_checking.head()


cross_checking['Error'] = cross_checking['Actual'] - cross_checking['Predicted']
cross_checking.head()


cross_checking_final  = cross_checking[cross_checking['Error'] <= 20]
cross_checking_final.sample(25).style.background_gradient(
        cmap='coolwarm').set_properties(**{
            'font-family': 'Lucida Calligraphy',
            'color': 'LigntGreen',
            'font-size': '15px'
        })


from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, r2_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Standardize the data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Build the MLP model
mlp_model = Sequential()
mlp_model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
mlp_model.add(Dense(32, activation='relu'))
mlp_model.add(Dense(1))

# Compile the model
mlp_model.compile(loss='mse', optimizer='adam')

# Train the model
mlp_model.fit(X_train_scaled, y_train, epochs=50, batch_size=10, verbose=1)

# Make predictions
y_pred_mlp = mlp_model.predict(X_test_scaled)

# Evaluate the model
mse_mlp = mean_squared_error(y_test, y_pred_mlp)
r2_mlp = r2_score(y_test, y_pred_mlp)
print(f'MLP Model - MSE: {mse_mlp}, R2 Score: {r2_mlp}')

Epoch 1/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 277us/step - loss: 49272.4258
Epoch 2/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 264us/step - loss: 17.8594
Epoch 3/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 264us/step - loss: 1.5824
Epoch 4/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 306us/step - loss: 1.3549
Epoch 5/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 266us/step - loss: 1.9260
Epoch 6/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 264us/step - loss: 0.8639
Epoch 7/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 264us/step - loss: 1.1559
Epoch 8/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 275us/step - loss: 1.3298
Epoch 9/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 1.7006
Epoch 10/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 1.3854
Epoch 11/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 275us/step - loss: 1.3155
Epoch 12/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 0.9738
Epoch 13/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 266us/step - loss: 0.7780
Epoch 14/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 0.9318
Epoch 15/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 276us/step - loss: 0.9338
Epoch 16/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 0.8152
Epoch 17/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 265us/step - loss: 0.6181
Epoch 18/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 276us/step - loss: 1.6709
Epoch 19/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 267us/step - loss: 0.9457
Epoch 20/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 294us/step - loss: 1.6886
Epoch 21/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 276us/step - loss: 1.5974
Epoch 22/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 266us/step - loss: 0.6508
Epoch 23/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 266us/step - loss: 1.4363
Epoch 24/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 267us/step - loss: 1.3786
Epoch 25/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 279us/step - loss: 0.9376
Epoch 26/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 268us/step - loss: 0.8532
Epoch 27/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.9270
Epoch 28/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 268us/step - loss: 0.7021
Epoch 29/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.8422
Epoch 30/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 281us/step - loss: 1.5426
Epoch 31/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.9313
Epoch 32/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 268us/step - loss: 0.8556
Epoch 33/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 270us/step - loss: 1.1805
Epoch 34/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 280us/step - loss: 0.7001
Epoch 35/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 271us/step - loss: 0.7493
Epoch 36/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 299us/step - loss: 1.5413
Epoch 37/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 288us/step - loss: 0.7257
Epoch 38/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.6937
Epoch 39/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.8396
Epoch 40/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 271us/step - loss: 0.7010
Epoch 41/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 282us/step - loss: 0.7524
Epoch 42/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 270us/step - loss: 0.9560
Epoch 43/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 0.5954
Epoch 44/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 280us/step - loss: 0.8679
Epoch 45/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 268us/step - loss: 1.2432
Epoch 46/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 270us/step - loss: 1.5490
Epoch 47/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 281us/step - loss: 0.8518
Epoch 48/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 269us/step - loss: 1.0041
Epoch 49/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 1s 270us/step - loss: 1.1406
Epoch 50/50
5502/5502 ━━━━━━━━━━━━━━━━━━━━ 2s 281us/step - loss: 0.4706
430/430 ━━━━━━━━━━━━━━━━━━━━ 0s 237us/step
MLP Model - MSE: 0.17525856311964316, R2 Score: 0.999998865808189

	DATE_TIME	PLANT_ID	SOURCE_KEY	DC_POWER	AC_POWER	DAILY_YIELD	TOTAL_YIELD
10508	20-05-2020 04:30	4135001	adLQvlD726eNBSB	0.000000	0.000000	0.000000	6304513.000000
54847	11-06-2020 09:00	4135001	uHbuxQJl8lW7ozc	3864.250000	379.737500	502.625000	7241010.625000
65902	16-06-2020 14:45	4135001	WRmjgnKYAwPKWDb	7727.125000	755.975000	5198.250000	7266513.250000
8416	19-05-2020 03:15	4135001	wCURE6d3bPkepu2	0.000000	0.000000	0.000000	6808393.000000
28319	29-05-2020 16:30	4135001	ih0vzX44oOqAx2f	4015.714286	394.571429	7615.857143	6294785.857000

	DATE_TIME	PLANT_ID	SOURCE_KEY	AMBIENT_TEMPERATURE	MODULE_TEMPERATURE	IRRADIATION
3141	2020-06-17 13:45:00	4135001	HmiyD2TTLFNqkNe	29.057731	45.051337	0.599461
704	2020-05-22 22:30:00	4135001	HmiyD2TTLFNqkNe	23.547205	22.648830	0.000000
3068	2020-06-16 19:30:00	4135001	HmiyD2TTLFNqkNe	24.442285	23.068772	0.000000
3149	2020-06-17 15:45:00	4135001	HmiyD2TTLFNqkNe	23.556775	23.536192	0.085784
890	2020-05-24 22:30:00	4135001	HmiyD2TTLFNqkNe	24.650606	23.198922	0.000000

	DATE_TIME	SOURCE_KEY	DC_POWER	AC_POWER	DAILY_YIELD	TOTAL_YIELD	AMBIENT_TEMPERATURE	MODULE_TEMPERATURE	IRRADIATION
52827	2020-06-10 10:15:00	7JYdWkrLSPkdwr4	6629.625000	649.375000	1699.625000	7796428.625000	27.128528	38.266939	0.480099
18586	2020-05-24 16:30:00	WRmjgnKYAwPKWDb	3600.500000	353.000000	7716.625000	7100742.625000	32.829294	40.124707	0.242545
46364	2020-06-07 08:45:00	ZnxXDlPa8U1GXgE	5111.000000	501.225000	585.125000	6693639.125000	23.404236	31.958346	0.341317
32196	2020-05-31 13:30:00	3PZuoBAID5Wc2HD	5162.625000	505.587500	4335.875000	7113823.875000	30.101738	48.065961	0.411193
29324	2020-05-30 04:45:00	iCRJl6heRkivqQ3	0.000000	0.000000	0.000000	7291253.000000	21.293420	20.270673	0.000000

	DC_POWER	AC_POWER	DAILY_YIELD	TOTAL_YIELD	AMBIENT_TEMPERATURE	MODULE_TEMPERATURE	IRRADIATION	DAY	MONTH	WEEK	MINUTES	TOTAL MINUTES PASS
count	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000	68774.000000
mean	3147.177450	307.778375	3295.834644	6978727.511362	25.558521	31.244997	0.232305	15.762876	5.518539	22.549481	22.490621	716.515107
std	4036.441826	394.394865	3145.220597	416270.720885	3.361300	12.308283	0.301948	8.554460	0.499660	1.461138	16.772385	412.069969
min	0.000000	0.000000	0.000000	6183645.000000	20.398505	18.140415	0.000000	1.000000	5.000000	20.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	6512006.826000	22.724491	21.123944	0.000000	9.000000	5.000000	21.000000	0.000000	360.000000
50%	428.571429	41.450000	2658.473214	7146685.000000	24.670178	24.818984	0.031620	16.000000	6.000000	23.000000	15.000000	720.000000
75%	6365.468750	623.561161	6274.000000	7268751.397000	27.960429	41.693659	0.454880	23.000000	6.000000	24.000000	30.000000	1065.000000
max	14471.125000	1410.950000	9163.000000	7846821.000000	35.252486	65.545714	1.221652	31.000000	6.000000	25.000000	45.000000	1425.000000

	Actual	Predicted
43819	0.0000	0.000000
2949	1072.3250	1071.243036
33769	299.8125	299.621357
47825	0.0000	0.000000
29370	0.0000	0.000000

	DATE_TIME	SOURCE_KEY	DC_POWER	AC_POWER	DAILY_YIELD	TOTAL_YIELD	AMBIENT_TEMPERATURE	MODULE_TEMPERATURE	IRRADIATION	DATE	TIME	DAY	MONTH	WEEK	HOURS	MINUTES	TOTAL MINUTES PASS	DATE_STRING
0	2020-05-15	1BY6WEcLGh8j5v7	0.0	0.0	0.0	6259559.0	25.184316	22.857507	0.0	2020-05-15	00:00:00	15	5	20	0	0	0	2020-05-15
1	2020-05-15	1IF53ai7Xc0U56Y	0.0	0.0	0.0	6183645.0	25.184316	22.857507	0.0	2020-05-15	00:00:00	15	5	20	0	0	0	2020-05-15

	Actual	Predicted	Error
66617	0.000000	0.000000	0.000000
19323	0.000000	0.000000	0.000000
22096	817.942857	817.714625	0.228232
51487	0.000000	0.000000	0.000000
28800	0.000000	0.000000	0.000000
37613	0.000000	0.000000	0.000000
64553	0.000000	0.000000	0.000000
68601	0.000000	0.000000	0.000000
812	556.675000	556.546560	0.128440
41193	0.000000	0.000000	0.000000
51656	0.000000	0.000000	0.000000
40767	842.975000	842.777032	0.197968
15312	0.000000	0.000000	0.000000
68029	90.300000	90.308000	-0.008000
20974	0.000000	0.000000	0.000000
22593	498.537500	498.462643	0.074857
54320	0.000000	0.000000	0.000000
55268	666.642857	666.609250	0.033607
15992	838.875000	839.413482	-0.538482
26192	618.342857	618.319089	0.023768
21612	0.000000	0.000000	0.000000
5465	86.214286	86.242732	-0.028446
37832	0.000000	0.000000	0.000000
36256	628.237500	628.244768	-0.007268
40239	673.285714	673.324286	-0.038571

Importing Power Generation & Weather Sensor Data¶

Adjust Datetime format¶

Merge the weather and sensor data¶

Observation: A huge amount of power is getting lost at the inverters. Only 1/10th of DC Power is getting converted to AC Power.¶

Separate time and date columns¶

Converting 'SOURCE_KEY' from categorical form to numerical form¶

Data Visualization¶

1. DC & AC Power plots over all 34 days¶

Observations:¶

DC Low Fluctuations: 2020-05-16, 2020-05-24, 2020-05-25, 2020-06-04¶

DC High Fluctuations: 2020-05-15, 2020-05-17, 2020-05-21, 2020-05-26, 2020-05-27, 2020-06-09, 2020-06-08, 2020-06-11, 2020-06-12, 2020-06-13¶

DC Very High Fluctuations: 2020-05-19, 2020-05-20, 2020-05-31, 2020-06-05, 2020-06-06, 2020-06-17¶

We can see that AC Power follows the same trend as DC Power because it is generated by DC power but it is 1/10th of the DC Power¶

2. DC Power & AC Power generated per day, sorted by descending order¶

Observations:¶

Highest DC_POWER Generation is on: 2020-05-25¶

Lowest DC_POWER Generation is on : 2020-05-18¶

3. Irradiation Plots over all 34 days¶

Observations:¶

Highest Irradiation is on: 2020-05-25¶

Lowest Irradiation is on : 2020-06-18¶

these graphs are similar to DC_Output graphs because irradiation is the amount of sunlight that falls on unit m2. this is directly proportional to the dc output produced. Huge amounts of DC power is generated with very little irradiation¶

4. Ambient & Module Temperature plots¶

Observation:¶

Module temperature is much higher than Ambient Temperature so probably Module temperature is not highly influenced by Ambient Temperature¶

5. DC Power /AC Power/ Irradiation / Ambient Vs Module Temperature¶

Highest DC_POWER is generated on "2020-05-25"¶

NOTE: Both DC_POWER graph and IRRADIATION graph is almost looking like an ideal graph which is explained earlier. Weather is also looking good, and there is no cloud is in the sky because there is very less variation in IRRADIATION and temperature of the solar panel and ambient temperature.¶

Lowest average DC_POWER is generated on "2020-05-18"¶

6. AC Power vs Inverter Plot for a particular date and time¶

Observation:¶

Here we can see that every inverter performs badly in converting DC to AC power efficiently.¶

Solar Power Plant Inverter Efficiency Calculation¶

Observation:¶

The efficiency of the plant is very poor. This is likely due to the fact that DC power is not getting converted to AC Power efficiently.¶

Q1. Can we identify the need for panel cleaning/maintenance?¶

Q2. Can we identify faulty or suboptimally performing equipment?¶

As we have observed through the AC/DC plots, there is a huge power loss at the inverter level. The AC output is almost 1/10th of the DC input. There is an issue with every inverter because this is the case with every AC observation. This may point to faulty equipment.¶

Q3. Can we predict the power generation for the next couple of days?¶

Linear Regression¶

Random Forest¶

Decision Tree¶

Result predictions¶